# from pyod.models.knn import KNN   # kNN detector
#
# contamination = 0.1  # percentage of outliers
# n_train = 200  # number of training points
# n_test = 100  # number of testing points
#
# X_train, y_train, X_test, y_test = generate_data(
#     n_train=n_train, n_test=n_test, contamination=contamination)
#
# # train kNN detector
# clf_name = 'KNN'
# clf = KNN()
# clf.fit(X_train)
#
# # get the prediction labels and outlier scores of the training data
# y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
# y_train_scores = clf.decision_scores_  # raw outlier scores
#
# # get the prediction on the test data
# y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
# y_test_scores = clf.decision_function(X_test)  # outlier scores

import pyshark

cap = pyshark.FileCapture( 'C:/Users/lzc/Desktop/anomalous-vertices-detection-master/data/dia_frag.pcap')
print(cap)